Synthesizer.LLVM.Parameterized.Signal

Documentation

data T p a

Constructors

forall context state ioContext parameters . (Storable parameters, MakeValueTuple parameters, C (ValueTuple parameters), C context, C state) => Cons (forall r c. Phi c => context -> state -> T r c (a, state)) (forall r. ValueTuple parameters -> CodeGenFunction r (context, state)) (forall r. context -> state -> CodeGenFunction r ()) (p -> IO (ioContext, parameters)) (ioContext -> IO ())

Instances

Functor (T p)

Applicative (T p)

ZipList semantics

(Field a, Real a, RationalConstant a) => Fractional (T p a)

(PseudoRing a, Real a, IntegerConstant a) => Num (T p a)

(Field a, RationalConstant a) => C (T p a)

(PseudoRing a, IntegerConstant a) => C (T p a)

Additive a => C (T p a)

simple :: (Storable parameters, MakeValueTuple parameters, C (ValueTuple parameters), C context, C state) => (forall r c. Phi c => context -> state -> T r c (al, state)) -> (forall r. ValueTuple parameters -> CodeGenFunction r (context, state)) -> T p parameters -> T p al

map :: (Storable ph, MakeValueTuple ph, ValueTuple ph ~ pl, C pl) => (forall r. pl -> a -> CodeGenFunction r b) -> T p ph -> T p a -> T p b

mapSimple :: (forall r. a -> CodeGenFunction r b) -> T p a -> T p b

zipWith :: (Storable ph, MakeValueTuple ph, ValueTuple ph ~ pl, C pl) => (forall r. pl -> a -> b -> CodeGenFunction r c) -> T p ph -> T p a -> T p b -> T p c

zipWithSimple :: (forall r. a -> b -> CodeGenFunction r c) -> T p a -> T p b -> T p c

iterate :: (Storable ph, MakeValueTuple ph, ValueTuple ph ~ pl, C pl, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al) => (forall r. pl -> al -> CodeGenFunction r al) -> T p ph -> T p a -> T p al

zip :: T p a -> T p b -> T p (a, b)

timeline edit

tail :: T p a -> T p a

tail empty generates the empty signal.

drop :: T p Int -> T p a -> T p a

append :: (Phi a, Undefined a) => T p a -> T p a -> T p a

Appending many signals is inefficient, since in cascadingly appended signals the parts are counted in an unary way. Concatenating infinitely many signals is impossible. If you want to concatenate a lot of signals, please render them to lazy storable vectors first.

cycle :: (Phi a, Undefined a) => T p a -> T p a

signal modifiers

interpolateConstant :: (C a, IsFloating b, IntegerConstant b, CmpRet b, CmpResult b ~ Bool, Storable b, MakeValueTuple b, ValueTuple b ~ Value b, FirstClass b, IsSized b, IsSized (Stored b)) => T p b -> T p a -> T p a

Stretch signal in time by a certain factor.

This can be used for doing expensive computations of filter parameters at a lower rate. Alternatively, we could provide an adaptive map that recomputes output values only if the input value changes, or if the input value differs from the last processed one by a certain amount.

mix :: Additive a => T p a -> T p a -> T p a

envelope :: PseudoRing a => T p a -> T p a -> T p a

envelopeStereo :: PseudoRing a => T p a -> T p (T a) -> T p (T a)

amplify :: (PseudoRing al, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al) => T p a -> T p al -> T p al

amplifyStereo :: (PseudoRing al, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al) => T p a -> T p (T al) -> T p (T al)

signal generators

constant :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al) => T p a -> T p al

exponentialCore :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al) => T p a -> T p a -> T p al

exponential2 :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ Value a, FirstClass a, IsSized a, IsSized (Stored a), IsArithmetic a, IsConst a) => T p a -> T p a -> T p (Value a)

exponentialBoundedCore :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, Real al) => T p a -> T p a -> T p a -> T p al

exponentialBounded2 :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ Value a, FirstClass a, IsSized a, IsSized (Stored a), Real a, IsConst a) => T p a -> T p a -> T p a -> T p (Value a)

Exponential curve that remains at the bound value if it would fall below otherwise. This way you can avoid extremal values, e.g. denormalized ones. The initial value and the bound value must be positive.

osciCore :: (Storable t, MakeValueTuple t, ValueTuple t ~ tl, C tl, Fraction tl) => T p t -> T p t -> T p tl

osci :: (Storable t, MakeValueTuple t, ValueTuple t ~ tl, Storable c, MakeValueTuple c, ValueTuple c ~ cl, C cl, C tl, Fraction tl, IntegerConstant tl) => (forall r. cl -> tl -> CodeGenFunction r y) -> T p c -> T p t -> T p t -> T p y

osciSimple :: (Storable t, MakeValueTuple t, ValueTuple t ~ tl, C tl, Fraction tl, IntegerConstant tl) => (forall r. tl -> CodeGenFunction r y) -> T p t -> T p t -> T p y

osciSaw :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, Fraction al, IntegerConstant al) => T p a -> T p a -> T p al

rampCore :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p a -> T p al

parabolaCore :: (Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p a -> T p a -> T p al

rampInf :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p al

parabolaFadeOutInf :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p al

parabolaFadeInInf :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p al

rampSlope :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, Additive al, IntegerConstant al) => T p a -> T p al

ramp :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, IntegerConstant al) => T p a -> T p al

parabolaFadeOutMap :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, IntegerConstant al) => T p a -> T p al

parabolaFadeInMap :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, IntegerConstant al) => T p a -> T p al

parabolaFadeOut :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, IntegerConstant al) => T p a -> T p al

parabolaFadeIn :: (C a, Storable a, MakeValueTuple a, ValueTuple a ~ al, C al, PseudoRing al, IntegerConstant al) => T p a -> T p al

noise :: (C a, IsFloating a, IsConst a, NumberOfElements a ~ D1, C (Value a), IsSized a, MakeValueTuple a, ValueTuple a ~ Value a, Storable a) => T p Word32 -> T p a -> T p (Value a)

noise seed rate

The rate parameter is for adjusting the amplitude such that it is uniform across different sample rates and after frequency filters. The rate is the ratio of the current sample rate to the default sample rate, where the variance of the samples would be one. If you want that at sample rate 22050 the variance is 1, then in order to get a consistent volume at sample rate 44100 you have to set rate = 2.

I use the variance as quantity and not the amplitude, because the amplitude makes only sense for uniformly distributed samples. However, frequency filters transform the probabilistic density of the samples towards the normal distribution according to the central limit theorem.

int31tofp :: (IsFloating a, NumberOfElements a ~ D1) => Value Word32 -> CodeGenFunction r (Value a)

noiseCore :: T p Word32 -> T p (Value Word32)

noiseCoreAlt :: T p Word32 -> T p (Value Word32)

conversion from and to storable vectors

fromStorableVector :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p (Vector a) -> T p value

fromStorableVectorLazy :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p (Vector a) -> T p value

piecewiseConstant :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p (T Int a) -> T p value

lazySize :: T p LazySize -> T p ()

Turns a lazy chunky size into a signal generator with unit element type. The signal length is the only information that the generator provides. Using zipWith you can use this signal as a lazy take.

derefFillPtr :: Importer (Ptr param -> Word32 -> Ptr a -> IO Word32)

run :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p value -> IO (Int -> p -> Vector a)

render :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p value -> Int -> p -> Vector a

This is not really a function, see renderChunky.

derefChunkPtr :: Importer (Ptr contextStateStruct -> Word32 -> Ptr struct -> IO Word32)

moduleChunky :: (C value, Struct value ~ struct, C parameters, Struct parameters ~ paramStruct, C context, C state, Struct (context, T state) ~ contextStateStruct) => (forall r z. Phi z => context -> state -> T r z (value, state)) -> (forall r. parameters -> CodeGenFunction r (context, state)) -> (forall r. context -> state -> CodeGenFunction r ()) -> CodeGenModule (Function (Ptr paramStruct -> IO (Ptr contextStateStruct)), Function (Ptr contextStateStruct -> IO ()), Function (Ptr contextStateStruct -> Word32 -> Ptr struct -> IO Word32))

compileChunky :: (C value, Struct value ~ struct, C parameters, Struct parameters ~ paramStruct, C context, C state, Struct (context, T state) ~ contextStateStruct) => (forall r z. Phi z => context -> state -> T r z (value, state)) -> (forall r. parameters -> CodeGenFunction r (context, state)) -> (forall r. context -> state -> CodeGenFunction r ()) -> IO (FunPtr (Ptr paramStruct -> IO (Ptr contextStateStruct)), FunPtr (Ptr contextStateStruct -> IO ()), FunPtr (Ptr contextStateStruct -> Word32 -> Ptr struct -> IO Word32))

debugMain :: forall parameters struct paramStruct contextStateStruct. (Storable parameters, IsType struct, IsType contextStateStruct, IsType paramStruct, IsSized paramStruct) => CodeGenModule (Function (Ptr paramStruct -> IO (Ptr contextStateStruct)), Function (Ptr contextStateStruct -> IO ()), Function (Ptr contextStateStruct -> Word32 -> Ptr struct -> IO Word32)) -> parameters -> IO (Function (Word32 -> Ptr (Ptr Word8) -> IO Word32))

runChunkyPattern :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p value -> IO (LazySize -> p -> Vector a)

Renders a signal generator to a chunky storable vector with given pattern. If the pattern is shorter than the generated signal this means that the signal is shortened.

runChunky :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => T p value -> IO (ChunkSize -> p -> Vector a)

renderChunky :: (Storable a, MakeValueTuple a, ValueTuple a ~ value, C value) => ChunkSize -> T p value -> p -> Vector a

This looks like a function, but it is not a function since it depends on LLVM being initialized with LLVM.initializeNativeTarget before. It is also problematic since you cannot control when and how often the underlying LLVM code is compiled. The compilation cannot be observed, thus it is referential transparent. But this influences performance considerably and I assume that you use this package exclusively for performance reasons.